Modeling perspective-taking upon observation of 3D biological motion
Modeling perspective-taking upon observation of 3D biological motion
复制标题
对 3D 生物运动观察的视角建模
DOI：
10.1109/devlrn.2014.6982998
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Martin Volker Butz
中科院分区：
文献类型：
--
作者：
Fabian Schrodt;Georg Layher;H. Neumann;Martin Volker Butz
在模仿学习的过程中，镜像神经系统似乎起着至关重要的作用。然而，目前尚不清楚镜像神经元的特性最初是如何发展的。发展镜像神经元的一个可能的先决条件可能是将观察到的运动转换成一个充分以自我为中心的参照系的能力。我们提出了一个人工神经网络(NN)模型，该模型通过高度具体化的方法实现了这种转换能力：该模型首先通过将自我中心视觉和本体感觉联系起来学习关联和预测自我诱导的运动模式。一旦这些预测足够准确，通过允许视觉参照系的自我监督、误差驱动的适应，对观察到的生物运动进行稳健和不变的识别成为可能。神经网络是一种改进的、动态的、自适应的共振模型，其特点是自我监督学习和调整、神经场归一化和信息驱动的神经噪声自适应。利用模拟的3D仿人步行器对所开发的建筑进行了评估，该机器人具有12个身体地标和10个角度自由度。该模型基本上展示了如何通过首先了解自己的身体运动动力学，然后通过观察其他相对的生物运动模式来利用这种自我知识，来获得用于推导另一个人的观点的内部参照框架。通过该模型获得的洞察力可能对社会能力的发展和相应的损伤具有重大影响。
It appears that the mirror neuron system plays a crucial role when learning by imitation. However, it remains unclear how mirror neuron properties develop in the first place. A likely prerequisite for developing mirror neurons may be the capability to transform observed motion into a sufficiently self-centered frame of reference. We propose an artificial neural network (NN) model that implements such a transformation capability by a highly embodied approach: The model first learns to correlate and predict self-induced motion patterns by associating egocentric visual and proprioceptive perceptions. Once these predictions are sufficiently accurate, a robust and invariant recognition of observed biological motion becomes possible by allowing a self-supervised, error-driven adaption of the visual frame of reference. The NN is a modified, dynamic, adaptive resonance model, which features self-supervised learning and adjustment, neural field normalization, and information-driven neural noise adaptation. The developed architecture is evaluated with a simulated 3D humanoid walker with 12 body landmarks and 10 angular DOF. The model essentially shows how an internal frame of reference adaptation for deriving the perspective of another person can be acquired by first learning about the own bodily motion dynamics and by then exploiting this self-knowledge upon the observation of other, relative, biological motion patterns. The insights gained by the model may have significant implications for the development of social capabilities and respective impairments.
影响因子：
56.9
作者：
ANDERSEN, RA;ESSICK, GK;SIEGEL, RM
通讯作者：
SIEGEL, RM